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Breeding & Genetics

Plant breeding is the art and science of changing the traits of plants in order to produce desired characteristics. Plant breeding can be accomplished through many different techniques ranging from simply selecting plants with desirable characteristics for propagation, to more complex molecular techniques.

Plant breeding started with sedentary agriculture and particularly the domestication of the first agricultural plants, a practice which is estimated to date back 9,000 to 11,000 years. Initially early farmers simply selected food plants with particular desirable characteristics, and employed these as progenitors for subsequent generations, resulting in an accumulation of valuable traits over time. Gregor Mendel's experiments with plant hybridization led to his establishing laws of inheritance. Once this work became well known, it formed the basis of the new science of genetics, which stimulated research by many plant scientists dedicated to improving crop production through plant breeding. Modern plant breeding is applied genetics, but its scientific basis is broader, covering molecular biology, cytology, systematics, physiology, pathology, entomology, chemistry, and statistics (biometrics).

Classical Breeding

Classical plant breeding uses deliberate interbreeding (crossing) of closely or distantly related individuals to produce new crop varieties or lines with desirable properties. Plants are crossbred to introduce traits/genes from one variety or line into a new genetic background. For example, a mildew-resistant pea may be crossed with a high-yielding but susceptible pea, the goal of the cross being to introduce mildew resistance without losing the high-yield characteristics. Progeny from the cross would then be crossed with the high-yielding parent to ensure that the progeny were most like the high-yielding parent, (backcrossing). The progeny from that cross would then be tested for yield and mildew resistance and high-yielding resistant plants would be further developed. Plants may also be crossed with themselves to produce inbred varieties for breeding. Classical breeding relies largely on homologous recombination between chromosomes to generate genetic diversity. The classical plant breeder may also make use of a number of in vitro techniques such as protoplast fusion, embryo rescue or mutagenesis (see below) to generate diversity and produce hybrid plants that would not exist in nature.

Traits that breeders have tried to incorporate into crop plants in the last 100 years include:

<ul><li>Preparation of EST data: Sequences were extracted from dbEST and were subjected to quality control screening (vector, E. coli, polyA, T, or CT removal, minimum length = 100 bp, &lt; 3% N).</li><li>Preparation of transcript (ET) database: All sequences from the appropriate divisions of GenBank (including RefSeq) were extracted. Non-coding sequences were discarded and cDNAs and coding sequences from genomic entries were saved. Sequences and related information (e.g. PubMed links) are stored in the qcGene database (qcGene).</li><li>Assembly: Cleaned EST sequences and non-redundant transcript (ET) sequences were combined. Using the Paracel Transcript Assembler Program, sequences were assembled into contigs. TCs are consensus sequences based on two or more ESTs (and possibly an ET) that overlap for at least 40 bases with at least 94% sequence identity. These strict criteria help minimize the creation of chimeric contigs. These contigs are assigned a TC (Tentative Consensus) number. TCs may comprise ESTs derived from different tissues. The best hits for TC's were assigned by searching the TC set against a non-redundant amino acid database(nraa) using BLAT. The top five hits based on score were selected and displayed for each TC.</li><li>Caveats: TCs are only as good as the ESTs underlying them; there may be unspliced or chimeric ESTs and thus TCs. There is still redundancy in the TC set because sequences must match end to end and at a certain percent identity to be combined. Directionality of the TCs should not be assumed. Not all TCs contain protein-coding regions.</li></ul>

Eight germplasm were chosen for this project:CDC WM-2, BAT 93, Expresso, Higuera-E, Jalo EEP-558, PI 430219, SMARC1N-PN1, and W6-15578. Tissue was collected from multiple plants at various developmental stages for RNA extraction which led to the generation of 3'-anchored cDNA libraries using the method described in Parkin et al., 2010. Each line was sequenced using the Roche 454 Titanium sequencing protocol. Sequencing reads were aligned directly to the Phaseolus vulgaris genomic build v0.9 using GMap. Then loci which were polymorphic between at least two of the lines were identified resulting in 133,108 SNPs. All SNPs were re-mapped to the published genome assembly 1.0 (Phytozome.org; Schmutz et al. 2014).

<p>Preparation of EST data: Sequences were extracted from dbEST and were subjected to quality control screening (vector, E. coli, polyA, T, or CT removal, minimum length = 100 bp, &lt; 3% N). Preparation of transcript (ET) database: All sequences from the appropriate divisions of GenBank (including RefSeq) were extracted. Non-coding sequences were discarded and cDNAs and coding sequences from genomic entries were saved. Sequences and related information (e.g. PubMed links) are stored in the qcGene database (qcGene). Assembly: Cleaned EST sequences and non-redundant transcript (ET) sequences were combined. Using the Paracel Transcript Assembler Program, sequences were assembled into contigs. TCs are consensus sequences based on two or more ESTs (and possibly an ET) that overlap for at least 40 bases with at least 94% sequence identity. These strict criteria help minimize the creation of chimeric contigs. These contigs are assigned a TC (Tentative Consensus) number. TCs may comprise ESTs derived from different tissues. The best hits for TC's were assigned by searching the TC set against a non-redundant amino acid database(nraa) using BLAT. The top five hits based on score were selected and displayed for each TC. Caveats: TCs are only as good as the ESTs underlying them; there may be unspliced or chimeric ESTs and thus TCs. There is still redundancy in the TC set because sequences must match end to end and at a certain percent identity to be combined. Directionality of the TCs should not be assumed. Not all TCs contain protein-coding regions.</p>

An Illumina Golden Gate array was developed using SNPs identified as part of the Pea 454 Sequencing & Genotyping Project. Loci where chosen such that the SNPs should be distributed evenly across the genome based on comparison to Medicago truncatula.

Sequencing reads were assembled into contigs using the NGen assembler resulting in 22,927 CDC Frontier contigs. Contigs from the other 10 lines were compared to CDC Frontier and loci which were polymorphic between CDC Frontier and at least one other line were identified resulting in 55,206 SNPs. However, it was later found that aligning the sequencing reads directly to the current genome build produced a much more reliable set of SNPs. As such, THESE SNPS SHOULD NOT BE USED; they are simply here for archival purposes.

Preparation of EST data: Sequences were extracted from dbEST and were subjected to quality control screening (vector, E. coli, polyA, T, or CT removal, minimum length = 100 bp, &lt; 3% N). Preparation of transcript (ET) database: All sequences from the appropriate divisions of GenBank (including RefSeq) were extracted. Non-coding sequences were discarded and cDNAs and coding sequences from genomic entries were saved. Sequences and related information (e.g. PubMed links) are stored in the qcGene database (qcGene). Assembly: Cleaned EST sequences and non-redundant transcript (ET) sequences were combined. Using the Paracel Transcript Assembler Program, sequences were assembled into contigs. TCs are consensus sequences based on two or more ESTs (and possibly an ET) that overlap for at least 40 bases with at least 94% sequence identity. These strict criteria help minimize the creation of chimeric contigs. These contigs are assigned a TC (Tentative Consensus) number. TCs may comprise ESTs derived from different tissues. The best hits for TC's were assigned by searching the TC set against a non-redundant amino acid database(nraa) using BLAT. The top five hits based on score were selected and displayed for each TC. Caveats: TCs are only as good as the ESTs underlying them; there may be unspliced or chimeric ESTs and thus TCs. There is still redundancy in the TC set because sequences must match end to end and at a certain percent identity to be combined. Directionality of the TCs should not be assumed. Not all TCs contain protein-coding regions.

The Medicago truncatula sequencing project was initiated with a generous grant from Samuel Roberts Noble Foundation to the University of Oklahoma. Beginning in 2003 (and renewed in 2006), the National Science Foundation and the European Union's Sixth Framework Programme provided funding to complete sequencing of the remaining euchromatic genespace. Among the eight chromosomes in Medicago, six are being sequenced by NSF project "Sequencing the Gene Space of the Model Legume, Medicago Truncatula," and two are being sequenced by partners in Europe. Nevin Young (University of Minnesota), Bruce Roe (ACGT, University of Oklahoma; chromosomes 1, 4, 6, 8), and Chris Town (TIGR; chromosomes 2, 7) are principal investigators of the U.S. project. In Europe, collaborators include Giles Oldroyd (John Innes Center) coordinating sequencing of chromosome 3 at the Sanger Center, and Frederic Deballe (INRA-CNRS) coordinating sequencing of chromosome 5 at Genoscope. The genome annotation was carried out by the International Medicago Genome Annotation Group (IMGAG), which involves participants from TIGR, INRA-CNRS, MIPS, UMN, Ghent University and NCGR.

Preparation of EST data: Sequences were extracted from dbEST and were subjected to quality control screening (vector, E. coli, polyA, T, or CT removal, minimum length = 100 bp, &lt; 3% N). Preparation of transcript (ET) database: All sequences from the appropriate divisions of GenBank (including RefSeq) were extracted. Non-coding sequences were discarded and cDNAs and coding sequences from genomic entries were saved. Sequences and related information (e.g. PubMed links) are stored in the qcGene database (qcGene). Assembly: Cleaned EST sequences and non-redundant transcript (ET) sequences were combined. Using the Paracel Transcript Assembler Program, sequences were assembled into contigs. TCs are consensus sequences based on two or more ESTs (and possibly an ET) that overlap for at least 40 bases with at least 94% sequence identity. These strict criteria help minimize the creation of chimeric contigs. These contigs are assigned a TC (Tentative Consensus) number. TCs may comprise ESTs derived from different tissues. The best hits for TC's were assigned by searching the TC set against a non-redundant amino acid database(nraa) using BLAT. The top five hits based on score were selected and displayed for each TC. Caveats: TCs are only as good as the ESTs underlying them; there may be unspliced or chimeric ESTs and thus TCs. There is still redundancy in the TC set because sequences must match end to end and at a certain percent identity to be combined. Directionality of the TCs should not be assumed. Not all TCs contain protein-coding regions.

Development of cultivars with improved nutritional profile and agronomic characters are among the major objectives in field pea breeding at the Crop Development Centre (CDC).In this project, 169 pea accessions of the cultivated pea Pisum sativum, wild relative species P. fulvum and several wild sub-species accessions (subspp. abyssinicum, arvense, and elatius) collected from eastern Europe, Russia and Canada were screened for their nutritional profile including total starch, amylose, amylopectin, fiber and protein by wet chemistry and/or near infrared (NIR) methods, and for reaction to ascochyta blight under controlled and/or field conditions.

Lentil is an economically important pulse crop for Canada produced mainly for the export market. In conventional breeding programs, several segregating generations must be grown in order to reach a certain level of homozygosity that allows the selection of traits of interest. In contrast, double-haploid (DH) technology produces instant homozygosity and thus can significantly reduce the time required for developing new varieties. The efficiency of the lentil breeding program will also be improved through the reduction in the population size required for screening.